Building Self-Correcting SQL Agents with LangGraph and Vanna AI in Python (2026 Tutorial)

Why Naive Text-to-SQL Fails in Enterprise Environments

Naive text-to-SQL prompts frequently invent non-existent column names, hallucinate incorrect table relationships, or generate SQL queries that time out on large datasets. Building an enterprise-grade natural language database assistant requires a self-correcting agent loop that generates SQL, executes it against an isolated read replica with strict timeouts, inspects compiler or execution errors, and iterates automatically until valid data returns.

Documented by Saizul Amin for AI engineers and enterprise data platform architects.

LangGraph Self-Correcting Execution Graph

from langgraph.graph import StateGraph, END
from typing import TypedDict

class SQLAgentState(TypedDict):
    natural_query: str
    generated_sql: str
    error_message: str
    query_result: list
    retry_count: int

def generate_sql_node(state: SQLAgentState):
    # LLM creates SQL using DDL context and previous error (if any)
    # ...
    return {"generated_sql": "SELECT COUNT(*) FROM orders WHERE status = '\''completed'\''", "retry_count": state["retry_count"] + 1}

def execute_sql_node(state: SQLAgentState):
    # Execute query against PostgreSQL read replica with 3-second statement_timeout
    # If error occurs, catch exception and store in error_message
    return {"query_result": [{"count": 4200}], "error_message": ""}

def check_execution(state: SQLAgentState):
    if not state["error_message"]:
        return END
    if state["retry_count"] >= 3:
        return END
    return "generate_sql"